Too Connected to Fail? Inferring Network Ties from Price Co-movements
Journal of Business and Economic Statistics,
We use extreme value theory methods to infer conventionally unobservable connections between financial institutions from joint extreme movements in credit default swap spreads and equity returns. Estimated pairwise co-crash probabilities identify significant connections among up to 186 financial institutions prior to the crisis of 2007/2008. Financial institutions that were very central prior to the crisis were more likely to be bailed out during the crisis or receive the status of systemically important institutions. This result remains intact also after controlling for indicators of too-big-to-fail concerns, systemic, systematic, and idiosyncratic risks. Both credit default swap (CDS)-based and equity-based connections are significant predictors of bailouts. Supplementary materials for this article are available online.
Information Feedback in Temporal Networks as a Predictor of Market Crashes
In complex systems, statistical dependencies between individual components are often considered one of the key mechanisms which drive the system dynamics observed on a macroscopic level. In this paper, we study cross-sectional time-lagged dependencies in financial markets, quantified by nonparametric measures from information theory, and estimate directed temporal dependency networks in financial markets. We examine the emergence of strongly connected feedback components in the estimated networks, and hypothesize that the existence of information feedback in financial networks induces strong spatiotemporal spillover effects and thus indicates systemic risk. We obtain empirical results by applying our methodology on stock market and real estate data, and demonstrate that the estimated networks exhibit strongly connected components around periods of high volatility in the markets. To further study this phenomenon, we construct a systemic risk indicator based on the proposed approach, and show that it can be used to predict future market distress. Results from both the stock market and real estate data suggest that our approach can be useful in obtaining early-warning signals for crashes in financial markets.
09.08.2017 • 29/2017
Vernetzt und aufgefangen
Während der Finanzkrise flossen Milliarden, um Banken zu retten, die ihren Regierungen zufolge zu groß waren als dass man sie hätte untergehen lassen dürfen. Doch eine Studie von Michael Koetter vom Leibniz-Institut für Wirtschaftsforschung Halle (IWH) und Ko-Autoren zeigt: Nicht nur die Größe der Bankhäuser war für eine Rettung entscheidend. Wesentlich war auch, wie zentral ein Institut im globalen Finanznetzwerk war.